Search Results for author: Haohao Hu

Found 8 papers, 0 papers with code

Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings

no code implementations28 Jul 2022 Miguel Ángel Muñoz-Bañón, Jan-Hendrik Pauls, Haohao Hu, Christoph Stiller, Francisco A. Candelas, Fernando Torres

Localization in aerial imagery-based maps offers many advantages, such as global consistency, geo-referenced maps, and the availability of publicly accessible data.

Large-Scale 3D Semantic Reconstruction for Automated Driving Vehicles with Adaptive Truncated Signed Distance Function

no code implementations28 Feb 2022 Haohao Hu, Hexing Yang, Jian Wu, Xiao Lei, Frank Bieder, Jan-Hendrik Pauls, Christoph Stiller

Since a 3D surface can be usually observed from multiple camera images with different view poses, an optimal image patch selection for the texturing and an optimal semantic class estimation for the semantic mapping are still challenging.

3D Reconstruction

Learned Enrichment of Top-View Grid Maps Improves Object Detection

no code implementations2 Mar 2020 Sascha Wirges, Ye Yang, Sven Richter, Haohao Hu, Christoph Stiller

We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input.

Object object-detection +1

Localization in Aerial Imagery with Grid Maps using LocGAN

no code implementations4 Jun 2019 Haohao Hu, Junyi Zhu, Sascha Wirges, Martin Lauer

In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps.

Accurate Global Trajectory Alignment using Poles and Road Markings

no code implementations25 Mar 2019 Haohao Hu, Marc Sons, Christoph Stiller

To bypass the flaws from direct incorporation of GNSS measurements for geo-referencing, the usage of aerial imagery seems promising.

An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps

no code implementations23 Feb 2018 Jannik Quehl, Haohao Hu, Sascha Wirges, Martin Lauer

In this paper, we present a new approach to vehicle trajectory prediction based on automatically generated maps containing statistical information about the behavior of traffic participants in a given area.

Trajectory Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.